PGC-1α-induced improvements in skeletal muscle metabolism and insulin sensitivityThis paper is one of a selection of papers published in this Special Issue, entitled 14th International Biochemistry of Exercise Conference – Muscles as Molecular and Metabolic Machines, and has undergone the Journal’s usual peer review process.
Bibliographic record
Abstract
The peroxisome proliferator-activated receptor gamma (PPARgamma) coactivator 1alpha (PGC-1alpha), a nuclear encoded transcriptional coactivator, increases the expression of many genes in skeletal muscle, including those involved with fatty acid oxidation and oxidative phosphorylation. Exercise increases the expression of PGC-1alpha, and the exercise-induced upregulation of many genes is attributable, in part, to the preceding activation and upregulation of PGC-1alpha. Indeed, PGC-1alpha overexpression, like exercise training, increases exercise performance. PGC-1alpha reductions in humans have been observed in type 2 diabetes, while, in cell lines, PGC-1alpha mimics the exercise-induced improvement in insulin sensitivity. However, unexpectedly, in mammalian muscle, PGC-1alpha overexpression contributed to the development of diet-induced insulin resistance. This may have been related to the massive overexpression of PGC-1alpha, which induced the upregulation of the fatty acid transporter FAT/CD36 and led to an increase in intramuscular lipids, which interfere with insulin signalling. In contrast, when PGC-1alpha was overexpressed modestly, within physiological limits, mitochondrial fatty acid oxidation was increased, GLUT4 expression was upregulated, and insulin-stimulated glucose transport was increased. More recently, similar PGC-1alpha-induced improvements in the insulin-resistant skeletal muscle of obese Zucker rats have been observed. These studies suggest that massive PGC-1alpha overexpression, but not physiologic PGC-1alpha overexpression, induces deleterious metabolic effects, and that exercise-induced improvements in insulin sensitivity are induced, in part, by the exercise-induced upregulation of PGC-1alpha.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".